Application of CNN in condition monitoring of high-voltage switchgear

Study explores the transformative application of Convolutional Neural Networks (CNN) in high-voltage switchgear state monitoring, addressing the critical need for precision and efficiency in power distribution monitoring. Specifically tailored CNN architectures demonstrate effectiveness in accurately detecting and classifying diverse high-voltage switchgear states and fault conditions. The experimental phase encompasses real-world scenarios, ensuring a comprehensive evaluation of the proposed approach. Results, including precision, recall, and F1-score metrics, affirm the reliability and effectiveness of the developed methodology. This research significantly advances power distribution monitoring, presenting CNN as a promising alternative to conventional techniques. Future prospects involve continuous refinement of CNN architectures and strategic integration of cutting-edge technologies, aiming to enhance the overall performance and adaptability of high-voltage switchgear monitoring systems.